Microfluidic synthesis of pH-responsive molecularly imprinted silica nanospheres for fluorescence sensing target glycoprotein.

Here, fluorescent artificial antibodies for sensing ovalbumin in food were synthesized by molecular imprinting technique in a microfluidic reactor. A phenylboronic acid-functionalized silane was employed as the functional monomer to enable the polymer has pH-responsive property. Fluorescent molecularly imprinted polymers (FMIPs) could be produced continuously in a short time. Both fluorescein isothiocyanate (FITC) and rhodamine B isothiocyanate (RB)-based FMIPs can specifically recognize the target ovalbumin, particularly FITC-based FMIP, giving an imprinting factor of 2.5 and cross-reactivity factors of 2.7 (ovotransferrin), 2.8 (ß-lactoglobulin) and 3.4 (bovine serum albumin), and was applied for the detection of ovalbumin in milk powder with recovery rates of 93-110%; moreover, the FMIP can be reused at least four times. Such FMIPs have promising future in replacing the fluorophore-labelled antibodies to fabricate fluorescent sensing devices or establish immunoassay methods, which have extra merits of low-cost, high stability and recyclability, easy to carry and store at ambient environments.

Simultaneous, Label-Free and High-throughput SERS Detection of Multiple Pesticides on Ag@Three-Dimensional Silica Photonic Microsphere Array.

Rapid identification and quantitative simultaneous analysis for multiple pesticide in real samples based on surface-enhanced Raman spectroscopy (SERS) is still a challenge because of sample complexity, reproducibility, and stability of SERS substrate. With use of colloidal silver nanoparticles loaded three-dimensional (3D) silica photonic microspheres (SPMs) array as the analytical platform, a SERS-based array assay for multiple pesticides was developed in this work. The silver nanoparticles were fixed into the gaps formed by the self-assembled nanospheres of the 3D SPMs to produce "hot spots", on which the Raman enhanced effect was up to 9.86 × 107 and the maximum electric field enhancement effect reached to 9.75 times, ensuring the target pesticides on the surface of the SERS-substrate integrated SPM can be detected sensitively. Using 2,4-dichlorophenoxyacetic acid (2,4-D), glyphosate, and imidacloprid as the testing pesticides, the label-free and high-throughput SERS assay for simultaneous detection of the pesticides was established, giving good linear detection ranges (0.1-204.8 µg/mL for 2,4-D, 0.3-247.9 µg/mL for glyphosate, and 0.2-204.8 µg/mL for imidacloprid) and low detection limits (3.03 ng/mL for 2,4-D, 3.14 ng/mL for glyphosate, and 8.82 ng/mL for imidacloprid). The spiked recovery rates in the real samples were measured in the range of 82-112%, which was consistent with that of the classical standard methods. The label-free 3D SERS array analytical platform provides a powerful tool for high-throughput and low-cost screening of multiple pesticide residues in real samples.

Study on the Construction of a Time-Space Four-Dimensional Combined Imaging Model and Moving Target Location Prediction Model.

Time-space four-dimensional motion target localization is a fundamental and challenging task in the field of intelligent driving, and an important part of achieving the upgrade in existing target localization technologies. In order to solve the problem of the lack of localization of moving targets in a spatio-temporal four-dimensional environment in the existing spatio-temporal data model, this paper proposes an optical imaging model in the four-dimensional time-space system and a mathematical model of the object-image point mapping relationship in the four-dimensional time-space system based on the central perspective projection model, combined with the one-dimensional "time" and three-dimensional "space". After adding the temporal dimension, the imaging system parameters are extended. In order to solve the nonlinear mapping problem of complex systems, this paper proposes to construct a time-space four-dimensional object-image mapping relationship model based on a BP artificial neural network and demonstrates the feasibility of the joint time-space four-dimensional imaging model theory. In addition, indoor time-space four-dimensional localization prediction experiments verify the performance of the model in this paper. The maximum relative error rates of the predicted motion depth values, time values, and velocity values of this localization method compared with the real values do not exceed 0.23%, 2.03%, and 1.51%, respectively.